##Pre-requisites - installing packages

#Loading the rvest,tidyverse and plotly package
library('rvest')
library(tidyverse)
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## ✓ tidyr   1.1.3     ✓ stringr 1.4.0
## ✓ readr   1.4.0     ✓ forcats 0.5.1
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library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
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##     layout

###Scrape the IMDB website to create a dataframe of information from 2016 top 100 movies

#Specifying the url for desired website to be scraped
url <- 'http://www.imdb.com/search/title?count=100&release_date=2016,2016&title_type=feature'

#Reading the HTML code from the website
webpage <- read_html(url)

Scrape the following data from the website

Rank: The rank of the film from 1 to 100 on the list of 100 most popular feature films released in 2016. Title: The title of the feature film. Description: The description of the feature film. Runtime: The duration of the feature film. Genre: The genre of the feature film, Rating: The IMDb rating of the feature film. Metascore: The metascore on IMDb website for the feature film. Votes: Votes cast in favor of the feature film. Gross_Earning_in_Mil: The gross earnings of the feature film in millions. Director: The main director of the feature film. Note, in case of multiple directors, I’ll take only the first. Actor: The main actor in the feature film. Note, in case of multiple actors, I’ll take only the first.

Ranking

#Using CSS selectors to scrape the rankings section
rank_data_html <- html_nodes(webpage,'.text-primary')

#Converting the ranking data to text
rank_data <- html_text(rank_data_html)

#Let's have a look at the rankings
head(rank_data)
## [1] "1." "2." "3." "4." "5." "6."

Converting ranking to numerical format

#Data-Preprocessing: Converting rankings to numerical
rank_data<-as.numeric(rank_data)

#Let's have another look at the rankings
head(rank_data)
## [1] 1 2 3 4 5 6

Scrape title

#Using CSS selectors to scrape the title section
title_data_html <- html_nodes(webpage,'.lister-item-header a')

#Converting the title data to text
title_data <- html_text(title_data_html)

#Let's have a look at the title
head(title_data)
## [1] "Suicide Squad"                      "Batman v Superman: Dawn of Justice"
## [3] "Captain America: Civil War"         "Captain Fantastic"                 
## [5] "Deadpool"                           "The Accountant"

scraping – Description, Runtime, Genre, Rating, Metascore, Votes, Gross_Earning_in_Mil , Director and Actor data.

#Using CSS selectors to scrape the description section
description_data_html <- html_nodes(webpage,'.ratings-bar+ .text-muted')

#Converting the description data to text
description_data <- html_text(description_data_html)

#Let's have a look at the description data
head(description_data)
## [1] "\n    A secret government agency recruits some of the most dangerous incarcerated super-villains to form a defensive task force. Their first mission: save the world from the apocalypse."                                                             
## [2] "\n    Fearing that the actions of Superman are left unchecked, Batman takes on the Man of Steel, while the world wrestles with what kind of a hero it really needs."                                                                                   
## [3] "\n    Political involvement in the Avengers' affairs causes a rift between Captain America and Iron Man."                                                                                                                                              
## [4] "\n    In the forests of the Pacific Northwest, a father devoted to raising his six kids with a rigorous physical and intellectual education is forced to leave his paradise and enter the world, challenging his idea of what it means to be a parent."
## [5] "\n    A wisecracking mercenary gets experimented on and becomes immortal but ugly, and sets out to track down the man who ruined his looks."                                                                                                           
## [6] "\n    As a math savant uncooks the books for a new client, the Treasury Department closes in on his activities, and the body count starts to rise."

Removing/n

#Data-Preprocessing: removing '\n'
description_data<-gsub("\n","",description_data)

#Let's have another look at the description data 
head(description_data)
## [1] "    A secret government agency recruits some of the most dangerous incarcerated super-villains to form a defensive task force. Their first mission: save the world from the apocalypse."                                                             
## [2] "    Fearing that the actions of Superman are left unchecked, Batman takes on the Man of Steel, while the world wrestles with what kind of a hero it really needs."                                                                                   
## [3] "    Political involvement in the Avengers' affairs causes a rift between Captain America and Iron Man."                                                                                                                                              
## [4] "    In the forests of the Pacific Northwest, a father devoted to raising his six kids with a rigorous physical and intellectual education is forced to leave his paradise and enter the world, challenging his idea of what it means to be a parent."
## [5] "    A wisecracking mercenary gets experimented on and becomes immortal but ugly, and sets out to track down the man who ruined his looks."                                                                                                           
## [6] "    As a math savant uncooks the books for a new client, the Treasury Department closes in on his activities, and the body count starts to rise."

Runtime

#Using CSS selectors to scrape the Movie runtime section
runtime_data_html <- html_nodes(webpage,'.text-muted .runtime')

#Converting the runtime data to text
runtime_data <- html_text(runtime_data_html)

#Let's have a look at the runtime
head(runtime_data)
## [1] "123 min" "152 min" "147 min" "118 min" "108 min" "128 min"

Data-Preprocessing: removing mins and converting it to numerical

runtime_data<-gsub(" min","",runtime_data)
runtime_data<-as.numeric(runtime_data)

#Let's have another look at the runtime data
head(runtime_data)
## [1] 123 152 147 118 108 128

Genre

#Using CSS selectors to scrape the Movie genre section
genre_data_html <- html_nodes(webpage,'.genre')

#Converting the genre data to text
genre_data <- html_text(genre_data_html)

#Let's have a look at the runtime
head(genre_data)
## [1] "\nAction, Adventure, Fantasy            "
## [2] "\nAction, Adventure, Sci-Fi            " 
## [3] "\nAction, Adventure, Sci-Fi            " 
## [4] "\nComedy, Drama            "             
## [5] "\nAction, Adventure, Comedy            " 
## [6] "\nAction, Crime, Drama            "
#Data-Preprocessing: removing \n
genre_data<-gsub("\n","",genre_data)

#Data-Preprocessing: removing excess spaces
genre_data<-gsub(" ","",genre_data)

#taking only the first genre of each movie
genre_data<-gsub(",.*","",genre_data)

#Convering each genre from text to factor
genre_data<-as.factor(genre_data)

#Let's have another look at the genre data
head(genre_data)
## [1] Action Action Action Comedy Action Action
## Levels: Action Adventure Animation Biography Comedy Crime Drama Horror

Ratings

#Using CSS selectors to scrape the IMDB rating section
rating_data_html <- html_nodes(webpage,'.ratings-imdb-rating strong')

#Converting the ratings data to text
rating_data <- html_text(rating_data_html)

#Let's have a look at the ratings
head(rating_data)
## [1] "6.0" "6.4" "7.8" "7.9" "8.0" "7.3"

Data-Preprocessing: converting ratings to numerical

#Data-Preprocessing: converting ratings to numerical
rating_data<-as.numeric(rating_data)

#Let's have another look at the ratings data
head(rating_data)
## [1] 6.0 6.4 7.8 7.9 8.0 7.3

Votes

#Using CSS selectors to scrape the votes section
votes_data_html <- html_nodes(webpage,'.sort-num_votes-visible span:nth-child(2)')

#Converting the votes data to text
votes_data <- html_text(votes_data_html)

#Let's have a look at the votes data
head(votes_data)
## [1] "612,095" "643,048" "676,028" "194,517" "913,733" "264,345"

Data-Preprocessing - Votes

#Data-Preprocessing: removing commas
votes_data<-gsub(",","",votes_data)

#Data-Preprocessing: converting votes to numerical
votes_data<-as.numeric(votes_data)

#Let's have another look at the votes data
head(votes_data)
## [1] 612095 643048 676028 194517 913733 264345

Directors

#Using CSS selectors to scrape the directors section
directors_data_html <- html_nodes(webpage,'.text-muted+ p a:nth-child(1)')

#Converting the directors data to text
directors_data <- html_text(directors_data_html)

#Let's have a look at the directors data
head(directors_data)
## [1] "David Ayer"     "Zack Snyder"    "Anthony Russo"  "Matt Ross"     
## [5] "Tim Miller"     "Gavin O'Connor"

data-preprocessing - directors

#Data-Preprocessing: converting directors data into factors
directors_data<-as.factor(directors_data)


head(directors_data)
## [1] David Ayer     Zack Snyder    Anthony Russo  Matt Ross      Tim Miller    
## [6] Gavin O'Connor
## 98 Levels: Adam Wingard Alex Proyas Ana Lily Amirpour ... Zack Snyder

Actors

#Using CSS selectors to scrape the actors section
actors_data_html <- html_nodes(webpage,'.lister-item-content .ghost+ a')

#Converting the gross actors data to text
actors_data <- html_text(actors_data_html)

#Let's have a look at the actors data
head(actors_data)
## [1] "Will Smith"      "Ben Affleck"     "Chris Evans"     "Viggo Mortensen"
## [5] "Ryan Reynolds"   "Ben Affleck"

data preprocessing - actors

#Data-Preprocessing: converting actors data into factors
actors_data<-as.factor(actors_data)

#Let's have a look at the actors data again
head(actors_data)
## [1] Will Smith      Ben Affleck     Chris Evans     Viggo Mortensen
## [5] Ryan Reynolds   Ben Affleck    
## 91 Levels: Aamir Khan Alexander Skarsgård Amy Adams ... Zach Galifianakis

Metascore

#Using CSS selectors to scrape the metascore section
metascore_data_html <- html_nodes(webpage,'.metascore')

#Converting the runtime data to text
metascore_data <- html_text(metascore_data_html)

#Let's have a look at the metascore 
head(metascore_data)
## [1] "40        " "44        " "75        " "72        " "65        "
## [6] "51        "

Data preprocessing

#Data-Preprocessing: removing extra space in metascore
metascore_data<-gsub(" ","",metascore_data)

#Lets check the length of metascore data
length(metascore_data)
## [1] 97

The length of the metascore data is 97 while we are scraping the data for 100 movies. The reason this happened is that there are 2 movies that don’t have the corresponding Metascore fields.

Fill missing metascores with NAs

for (i in c(18,57,74)){

a<-metascore_data[1:(i-1)]

b<-metascore_data[i:length(metascore_data)]

metascore_data<-append(a,list("NA"))

metascore_data<-append(metascore_data,b)

}
#Data-Preprocessing: converting metascore to numerical
metascore_data<-as.numeric(metascore_data)
## Warning: NAs introduced by coercion

## Warning: NAs introduced by coercion

## Warning: NAs introduced by coercion
#Let's have another look at length of the metascore data

length(metascore_data)
## [1] 100
summary(metascore_data)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   25.00   48.00   62.00   60.44   72.00   99.00       3

Gross Revenue

#Using CSS selectors to scrape the gross revenue section
gross_data_html <- html_nodes(webpage,'.ghost~ .text-muted+ span')

#Converting the gross revenue data to text
gross_data <- html_text(gross_data_html)

#Let's have a look at the votes data
head(gross_data)
## [1] "$325.10M" "$330.36M" "$408.08M" "$5.88M"   "$363.07M" "$86.26M"
#Data-Preprocessing: removing '$' and 'M' signs
gross_data<-gsub("M","",gross_data)

gross_data<-substring(gross_data,2,6)

#Let's check the length of gross data
length(gross_data)
## [1] 92

Filling missing entries with NA

#Filling missing entries with NA
for (i in c(18,67,73,75,83,87,98,100)){

a <- gross_data[1:(i-1)]
b <- gross_data[i:length(gross_data)]
gross_data <- append(a, -1) # used -1 in place of NA's
gross_data <- append(gross_data, b)
}
gross_data <- na.exclude(gross_data)
gross_data <- gross_data[-c(101)]
gross_data <- as.numeric(gross_data)

#Let's have another look at the length of gross data
length(gross_data)
## [1] 100
summary(gross_data)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   -1.00    9.93   46.69   83.94  102.58  532.10

Combine all the lists to form a data frame

movies_df<-data.frame(Rank = rank_data, Title = title_data,

Description = description_data, Runtime = runtime_data,

Genre = genre_data, Rating = rating_data,

Metascore = metascore_data, Votes = votes_data,                                     

Gross_Earning_in_Mil = gross_data,

Director = directors_data, Actor = actors_data)

#Structure of the data frame

str(movies_df)
## 'data.frame':    100 obs. of  11 variables:
##  $ Rank                : num  1 2 3 4 5 6 7 8 9 10 ...
##  $ Title               : chr  "Suicide Squad" "Batman v Superman: Dawn of Justice" "Captain America: Civil War" "Captain Fantastic" ...
##  $ Description         : chr  "    A secret government agency recruits some of the most dangerous incarcerated super-villains to form a defens"| __truncated__ "    Fearing that the actions of Superman are left unchecked, Batman takes on the Man of Steel, while the world "| __truncated__ "    Political involvement in the Avengers' affairs causes a rift between Captain America and Iron Man." "    In the forests of the Pacific Northwest, a father devoted to raising his six kids with a rigorous physical "| __truncated__ ...
##  $ Runtime             : num  123 152 147 118 108 128 120 116 107 116 ...
##  $ Genre               : Factor w/ 8 levels "Action","Adventure",..: 1 1 1 5 1 1 1 1 3 7 ...
##  $ Rating              : num  6 6.4 7.8 7.9 8 7.3 6.8 7.4 7.6 7.9 ...
##  $ Metascore           : num  40 44 75 72 65 51 67 70 81 81 ...
##  $ Votes               : num  612095 643048 676028 194517 913733 ...
##  $ Gross_Earning_in_Mil: num  325.1 330.3 408 5.88 363 ...
##  $ Director            : Factor w/ 98 levels "Adam Wingard",..: 23 98 6 61 93 36 40 86 82 27 ...
##  $ Actor               : Factor w/ 91 levels "Aamir Khan","Alexander Skarsgård",..: 89 8 19 88 75 8 39 73 7 3 ...

Question 1: Based on the above data, which movie from which Genre had the longest runtime?

p1 <- movies_df %>%
  ggplot(aes(x=Runtime, fill = Genre)) +
  geom_histogram(position="identity", alpha=0.7, binwidth = 6, color = "black")+
  scale_fill_discrete(name = "Genre") +
  labs(title = "Runtime by Genre of top 100 movies of 2016")  
ggplotly(p1)

Answer:

movies_df %>%
  rownames_to_column(var = "Name") %>% 
  filter(Runtime == max(Runtime))
##   Name Rank   Title
## 1   49   49 Silence
## 2   57   57  Dangal
##                                                                                                                                                                             Description
## 1     In the 17th century, two Portuguese Jesuit priests travel to Japan in an attempt to locate their mentor, who is rumored to have committed apostasy, and to propagate Catholicism.
## 2                              Former wrestler Mahavir Singh Phogat and his two wrestler daughters struggle towards glory at the Commonwealth Games in the face of societal oppression.
##   Runtime  Genre Rating Metascore  Votes Gross_Earning_in_Mil        Director
## 1     161  Drama    7.2        79 101721                 7.10 Martin Scorsese
## 2     161 Action    8.4        NA 159981                12.39   Nitesh Tiwari
##             Actor
## 1 Andrew Garfield
## 2      Aamir Khan

Question 2: Based on the above data, in the Runtime of 130-160 mins,which genre has the highest votes?

p2 <- movies_df %>%
  ggplot(aes(x=Runtime,y=Rating))+
  geom_point(aes(size=Votes,col=Genre, text = paste("Movie Title:", title_data)), alpha = 0.7) +
  labs(title = " Runtime by Ratings of top 100 movies of 2016")
## Warning: Ignoring unknown aesthetics: text
ggplotly(p2)

Answer:

movies_df %>%
  rownames_to_column(var = "Name") %>% 
  filter(Runtime >= 130 & Runtime <= 160) %>%
  filter(Votes == max(Votes))
##   Name Rank                      Title
## 1    3    3 Captain America: Civil War
##                                                                                              Description
## 1     Political involvement in the Avengers' affairs causes a rift between Captain America and Iron Man.
##   Runtime  Genre Rating Metascore  Votes Gross_Earning_in_Mil      Director
## 1     147 Action    7.8        75 676028                  408 Anthony Russo
##         Actor
## 1 Chris Evans

Question 3: Based on the above data, across all genres which genre has the highest average gross earnings in runtime 100 to 120.

p3 <- movies_df %>%
  ggplot(aes(x=Runtime,y=Gross_Earning_in_Mil))+
  geom_point(aes(size = Rating,col = Genre), alpha = 0.5) +
  labs(title = "Runtime by Gross Earnings in Millions of top 100 movies of 2016") +
  scale_y_continuous("Gross Earnings in Millions", limits =c(-10, 600))
ggplotly(p3)

Answer:

movies_df %>%
  rownames_to_column(var = "Name") %>% 
  filter(Runtime >= 100 & Runtime <= 120) %>%
  group_by(Genre) %>%
  summarize(averageGross = mean(Gross_Earning_in_Mil, na.rm = TRUE)) %>%
  filter(averageGross == max(averageGross))
## # A tibble: 1 x 2
##   Genre     averageGross
##   <fct>            <dbl>
## 1 Animation         216.